{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***Report 2-Titanic:Machine learining from Disaster***\n",
    "* 刘晚移\n",
    "* 2021260153\n",
    "\n",
    "\n",
    "## 一、任务简介\n",
    "\n",
    "### 1.任务描述\n",
    "* 任务类型：二元分类       \n",
    "* 背景介绍：泰坦尼克号沉船事件是历史上最著名的海难之一。 1912年4月15日，泰坦尼克号在处女航途中与冰山相撞后沉没，船上共2224名乘客和机组人员，1502人丧生。 这一骇人听闻的悲剧震惊了国际社会，并促使行业推出了更全面的船舶安全规定。  \n",
    "沉船事故导致人员丧生的原因之一是没有足够的救生艇供乘客和船员使用。尽管在事故中存活有一定的运气成分，但某些群体确实比其他群体更易存活下来，例如妇女，儿童和上层阶级。\n",
    "在这项挑战中，要求对可能生存的人群进行分析。要求使用机器学习工具来预测哪些乘客幸免于悲剧。      \n",
    "* 数据介绍：训练集中共有200000条样本，预测集中有80000条样本。\n",
    "\n",
    "### 2.数据变量说明\n",
    "**&nbsp;&nbsp;&nbsp;&nbsp;训练数据集有891行12列。各列代表的信息：**\n",
    "* PassengerId：一个用以标记每个乘客的数字id\n",
    "* Survived：标记乘客是否幸存——幸存(1)、死亡(0)。我们将预测这一列。\n",
    "* Pclass：船舱等级，分为1，2，3等 。\n",
    "* Name：乘客名字。\n",
    "* Sex：乘客性别——男male、女female\n",
    "* Age：乘客年龄。部分。\n",
    "* SibSp：船上兄弟姐妹和配偶的数量。\n",
    "* Parch：船上父母和孩子的数量。\n",
    "* Ticket：乘客的船票号码。\n",
    "* Fare：乘客为船票付了多少钱。\n",
    "* Cabin：乘客住在哪个船舱。\n",
    "* Embarked：乘客从哪个地方登上泰坦尼克号。\n",
    "\n",
    "\n",
    "\n",
    "### 3.评估方法\n",
    "\n",
    "你的提交结果为每个测试样本未通过审核的概率，也就是Evaluation为1的概率。评价方法为精度-召回曲线下面积(Precision-Recall AUC)，以下简称PR-AUC。 \n",
    "PR-AUC的取值范围是0到1。越接近1，说明模型预测的结果越接近真实结果。\n",
    "  \n",
    "### 4.任务目标\n",
    "\n",
    "编写程序用训练数据进行分类训练得到预测模型，再将测试数据输入该预测模型中进行预测得到预测结果，将预测结果进行评价，尽可能提高预测精度。\n",
    "\n",
    "\n",
    "## 二、任务思路\n",
    "1.加载样本      \n",
    "2.求出总数、总计、均值、方差    \n",
    "3.利用均值补全空白值    \n",
    "4.交叉验证，将训练数据做测试，123选中其二作为训练模型，剩下一个作为测试，原测试集不用。交叉训练验证取平均值)    \n",
    "5.线性回归\n",
    "\n",
    "## 三、算法步骤"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.导入库，数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'D:/data_analysis/jupyter_notebook/machine_learning/dataset//titannic/train.xls'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-d178a0982687>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mpath1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'D:/data_analysis/jupyter_notebook/machine_learning/dataset//titannic/test.xls'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mpath2\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'D:/data_analysis/jupyter_notebook/machine_learning/dataset//titannic/train.xls'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mdata_train\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[0mdata_test\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mdata_train\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Software\\Anaconda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    608\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 610\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    611\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    612\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Software\\Anaconda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    460\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    461\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 462\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    463\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    464\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Software\\Anaconda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    817\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    818\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Software\\Anaconda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1048\u001b[0m             )\n\u001b[0;32m   1049\u001b[0m         \u001b[1;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1050\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# type: ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1051\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1052\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Software\\Anaconda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1866\u001b[0m         \u001b[1;31m# open handles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1867\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1868\u001b[0m         \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1869\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"storage_options\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"encoding\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"memory_map\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"compression\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Software\\Anaconda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_open_handles\u001b[1;34m(self, src, kwds)\u001b[0m\n\u001b[0;32m   1360\u001b[0m         \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHanldes\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1361\u001b[0m         \"\"\"\n\u001b[1;32m-> 1362\u001b[1;33m         self.handles = get_handle(\n\u001b[0m\u001b[0;32m   1363\u001b[0m             \u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1364\u001b[0m             \u001b[1;34m\"r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Software\\Anaconda\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    640\u001b[0m                 \u001b[0merrors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"replace\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    641\u001b[0m             \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 642\u001b[1;33m             handle = open(\n\u001b[0m\u001b[0;32m    643\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    644\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'D:/data_analysis/jupyter_notebook/machine_learning/dataset//titannic/train.xls'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "path1='D:/data_analysis/jupyter_notebook/machine_learning/dataset//titannic/test.xls'\n",
    "path2='D:/data_analysis/jupyter_notebook/machine_learning/dataset//titannic/train.xls'\n",
    "data_train=pd.read_csv(path2)\n",
    "data_test=pd.read_csv(path1)\n",
    "data_train"
   ]
  },
  {
   "attachments": {
    "Titanic01.jpg": {
     "image/jpeg": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Titanic01.jpg](attachment:Titanic01.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Age这一列只有714，Embarked这一列只有889，说明需要进行缺失值填充；而Cabin这一列总数也就204，缺得太多了，可以考虑删除这一列；Sex、Embarked、Name等属性都是object类型，要将其转化为机器学习能处理的类型。这都是数据预处理的内容，无论哪个案例都需要数据预处理。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.数据测试"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Titanic02.jpg](attachment:Titanic02.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.数据空值处理\n",
    "* 客舱号Cabin列由于存在大量的空值，如果直接对空值进行填空，带来的误差影响会比较大，先不选用Cabin列做特征\n",
    "* 年龄列对于是否能够存活的判断很重要，采用Age均值对空值进行填充\n",
    "* PassengerId是一个连续的序列，对于是否能够存活的判断无关，不选用PassengerId作为特征\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train['Age']=data_train['Age'].fillna(data_train['Age'].median())\n",
    "#用年龄的中位数填充年龄空值\n",
    "data_train.describe()"
   ]
  },
  {
   "attachments": {
    "Titanic03.jpg": {
     "image/jpeg": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Titanic03.jpg](attachment:Titanic03.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#线性回归算法,\n",
    "#没有用到验证集\n",
    "from sklearn.linear_model import LinearRegression#线性回归\n",
    "from sklearn.model_selection import KFold#训练集交叉验证，得到平均值\n",
    "#选取简单的可用输入特征\n",
    "predictors=['Pclass','Age','SibSp','Parch','Fare']\n",
    "#初始化线性回归算法\n",
    "alg=LinearRegression()\n",
    "kf=KFold(n_splits=3,shuffle=False,random_state=1)#数据分成3份，交叉验证\n",
    "predictions=[]#存放所有结果\n",
    "for train,test in kf.split(data_train):#生成3个元组，for循环迭代这三个元组，得到train和test的索引\n",
    "    train_predictors=data_train[predictors].iloc[train,:]#根据五个特征选取当前训练集数据数据\n",
    "    train_target=data_train['Survived'].iloc[train]#训练目标是存活与否\n",
    "    alg.fit(train_predictors,train_target)#用线性回归算法训练数据\n",
    "    test_predictions=alg.predict(data_train[predictors].iloc[test,:])#预测测试集数据\n",
    "    predictions.append(test_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* k交叉验证（KFold）\n",
    "X是一个9 * 3的矩阵，即9个样本，3个特征，y是一个9维列向量，即9个标签。现在我要进行3折交叉验证。\n",
    "执行kFold = KFold(n_splits=3) ：其中KFold是一个类，n_split=3表示，当执行KFold的split函数后，数据集被分成三份，两份训练集和一份验证集。\n",
    "执行index = kFold.split(X=X)：index是一个生成器，每个元素是一个元组，元组里面有两个元素，第一个是训练集的索引，第二个是验证集的索引。因为这里将9个样本分成三份，所以index中有三个这样的元组\n",
    "之后便可以迭代index来获得训练集和验证集的索引，从而获得训练集和测试集了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.检查预测的准确性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=np.concatenate(predictions)#使用concatenate完成多个数组的拼接\n",
    "predictions[predictions>.5]=1\n",
    "predictions[predictions<=.5]=0\n",
    "accuracy=sum(predictions==data_train['Survived'])/len(predictions)\n",
    "print('准确率为：',accuracy)#0.7037037037037037"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 四、总结\n",
    "\n",
    "   线性回归算法是数据分类的经典算法之一，根据目前结果而言，不如作为标杆模型的随机森林算法，并且还有优化空间。通过这次任务再次梳理了线性回归算法的原理，熟悉了python语句和数据的结构，锻炼了分析问题和步骤规划的能力，让我对数据处理问题有了新的认识。"
   ]
  }
 ],
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